Big data applications, such as recommendation systems, predictive modeling, and pattern recognition, often rely on a search for data objects that are similar to a query object. Feature reduction is a common task in many statistical applications. Data features represented in higher dimensional spaces may exhibit varying levels of sparseness and different types of distributions. Feature reduction allows for extraction of data features that may be of interest.